Aron Walsh is a Full Professor and Fellow of the Royal Society of Chemistry (RSC) in the Department of Materials. He leads the Materials Design Group at the Thomas Young Centre in London and holds a Distinguished Visiting Professorship at Ewha Womans University in Seoul.
Aron was awarded his PhD in Chemistry from Trinity College Dublin. He then worked for the US Department of Energy at the National Renewable Energy Laboratory, followed by a Marie Curie Fellowship hosted by University College London, and a Royal Society University Research Fellowship held at the University of Bath.
His research involves cutting-edge materials theory and simulation applied to problems across solid-state chemistry and physics, including materials for solar cells and fuels, batteries, thermoelectrics, and solid-state lighting. He has expertise in the theory of semiconductors and dielectrics, and is developing innovative solutions for materials data, informatics and design. His group published a review on machine learning for molecules and materials in Nature.
These activities have been supported by funding from the Royal Society, EPSRC, and the European Research Council.
Aron was awarded the EU-40 prize from the European Materials Research Society and the Chemistry Society Reviews Emerging Investigator Lectureship for his work on the theory of next-generation perovskite photovoltaics. In 2017, he was a recipient of the Philip Leverhulme Prize. In 2019, he received the Corday-Morgan Prize from the RSC for his breakthrough research on hybrid organic-inorganic solids.
et al., 2024, A Hole-Selective Self-Assembled Monolayer for Both Efficient Perovskite and Organic Solar Cells., Langmuir
et al., 2024, Accelerated chemical science with AI., Digit Discov, Vol:3, Pages:23-33
et al., 2024, Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains., Small, Vol:20
Walsh A, 2024, Open computational materials science., Nat Mater, Vol:23, Pages:16-17
Tolborg K, Walsh A, 2023, Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields., J Phys Chem Lett, Vol:14, Pages:11618-11624